code: def test_config(self):
      e = copy.deepcopy(EXPERIMENT)
      e = ExperimentConfig(e)
      trainer = e.experiment["trainer"]
  
      self.assertIsInstance(trainer.model, TestModel)
      self.assertIsInstance(trainer.dataset_splits, TestDataset)
      self.assertIsInstance(
          trainer.loss, torch.nn.modules.loss.CrossEntropyLoss
      )
      self.assertIsInstance(trainer.optimizer, torch.optim.Adam)
      self.assertEqual(len(trainer.metrics), 3)
      self.assertEqual(trainer.device, torch.device(type="cpu"))
      self.assertEqual(trainer.seed, 1337)
      self.assertEqual(trainer.loss_accumulation_steps, 4)
      self.assertIsInstance(
          trainer.scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau
      )
      self.assertEqual(trainer.num_epochs, 5)
      self.assertIsInstance(trainer.regularizer, L1)
      self.assertEqual(trainer.gradient_clipping, 0.25)
      self.assertEqual(trainer.embeddings_name, None)
      self.assertEqual(
          trainer.forward_params, ["x_in", "apply_softmax"]
      )
  
file_path: transfer-nlp/tests/plugins/test_trainer.py
imports: import copy
  import unittest
  from pathlib import Path
  import ignite
  import ignite.metrics
  import torch.nn
  import torch.optim
  from ignite.metrics import Precision, Recall, MetricsLambda
  from transfer_nlp.plugins.config import ExperimentConfig
  from transfer_nlp.plugins.regularizers import L1
  from .trainer_utils import *
label_desc: Use image node_id 1 for calling the RegistryTest obj's underlying member method code with example usage: obj.test_config() without return types